Implement an End-to-End RAG Pipeline Using LangChain

Problem Implement an end-to-end Retrieval-Augmented Generation (RAG) pipeline using LangChain: ingest source documents and answer user queries grounded in them.

Functional requirements

• Load and chunk source documents from their raw format.
• Embed chunks and index them in a vector store.
• Retrieve the top-k relevant chunks for a query.
• Inject retrieved context into a prompt template before calling the LLM.
• Return the answer with citations back to the source chunks.

Non-functional requirements

• Corpus of ~100k-1M chunks; index rebuilt or incrementally updated as documents change.
• p95 query latency ~1-3s end-to-end, of which retrieval is tens of milliseconds.
• ~10-50 queries/sec; per-query token cost bounded by top-k and chunk size.
• Answers must be traceable — every claim maps back to a retrieved chunk.

Key components

• Document loaders plus a text splitter (recursive-character or token-based) with a chosen chunk size and overlap.
• Embedding model applied to every chunk at index time and to the query at read time.
• Vector store — FAISS or Chroma via LangChain's integrations — holding vectors plus metadata.
• Retriever fetching top-k by similarity, optionally with metadata filters.
• A chain wiring retrieved context into a prompt template ahead of the LLM call.
• Evaluation harness: a fixed question set scored on retrieval recall and answer groundedness.

Deep dives / trade-offs

• Chunking strategy: small chunks retrieve precisely but fragment context; large chunks preserve meaning but dilute the embedding and burn tokens — overlap as the compromise.
• Retrieval top-k tuning: too few and you miss the answer, too many and the model drowns in irrelevant context; re-ranking as the middle path.
• Prompt construction to keep the model grounded — instructing it to answer only from context and to admit when the context is insufficient.
• Pure vector search vs. hybrid dense + keyword retrieval when queries hinge on rare exact terms like IDs or product codes.